Image post-processing operations are generally not successful at recovering visual information (also referred to as image data) that is lost due to either under- or over-exposure when capturing those images. Consequently, real-time auto-exposure is a fundamental operation applied by most consumer cameras in an attempt to capture high-quality photographs with proper exposure settings. For example, smart-phone based cameras typical rely on simple metering over a predefined area or set of areas to find a suitable exposure. However, typical automated exposure systems often fail to provide acceptable image capture results in common scenarios.
Some commercial cameras apply various heuristic algorithms to detect faces or objects. Similarly, many cameras allow manual selection of region of interest (ROI) for local exposure adjustment. Typical metering techniques for performing automated exposure control tend to analyze an image or scene using intensity distributions or histograms that assume all content in the scene is equally important, or by considering other regions such as, for example, spot or matrix-based techniques. Such techniques may also consider fixed weighting based on a proximity to the image center, detected faces or objects, or some user selected focal point. As such, some of these cameras consider various types of scene semantics (e.g., face or object detection, etc.) when determining automated exposure correction or adjustments of a scene being photographed.
Unfortunately, in many back-lit situations, typical automated exposure control systems tend to produce poor contrast on the back-lit subject (e.g., a person, object, etc.) while providing good contrast of the background itself. For example, a person standing indoors in front of a bright window will tend to appear as a dark silhouette against that window given typical automated exposure control. Generally, this is the exact opposite of what the photographer would prefer, e.g., better contrast on the back-lit subject at the cost of the background being washed out. Conversely, a very dark background often causes a typical automated exposure control system to over-expose the foreground object of interest. In either case, use of incorrect exposure settings by the automated exposure control system results in the loss of image data.